{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython import display\n",
    "\n",
    "from dc_utils import Logger\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.optim import Adam\n",
    "from torch.autograd import Variable\n",
    "\n",
    "from torchvision import transforms, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FOLDER = './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mnist_data():\n",
    "    compose = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(64),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=[0.5], std=[0.5])\n",
    "        ])\n",
    "    out_dir = '{}/dataset'.format(DATA_FOLDER)\n",
    "    return datasets.MNIST(root=out_dir, train=True, transform=compose, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = mnist_data()\n",
    "batch_size = 100\n",
    "data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True)\n",
    "num_batches = len(data_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DiscriminativeNet(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(DiscriminativeNet, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(\n",
    "                in_channels=1, out_channels=128, kernel_size=4, \n",
    "                stride=2, padding=1, bias=False\n",
    "            ),\n",
    "            nn.LeakyReLU(0.2, inplace=True)\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(\n",
    "                in_channels=128, out_channels=256, kernel_size=4,\n",
    "                stride=2, padding=1, bias=False\n",
    "            ),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.LeakyReLU(0.2, inplace=True)\n",
    "        )\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(\n",
    "                in_channels=256, out_channels=512, kernel_size=4,\n",
    "                stride=2, padding=1, bias=False\n",
    "            ),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.LeakyReLU(0.2, inplace=True)\n",
    "        )\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.Conv2d(\n",
    "                in_channels=512, out_channels=1024, kernel_size=4,\n",
    "                stride=2, padding=1, bias=False\n",
    "            ),\n",
    "            nn.BatchNorm2d(1024),\n",
    "            nn.LeakyReLU(0.2, inplace=True)\n",
    "        )\n",
    "        self.out = nn.Sequential(\n",
    "            nn.Linear(1024*4*4, 1),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Convolutional layers\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        # Flatten and apply sigmoid\n",
    "        x = x.view(-1, 1024*4*4)\n",
    "        x = self.out(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GenerativeNet(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(GenerativeNet, self).__init__()\n",
    "        \n",
    "        self.linear = torch.nn.Linear(100, 1024*4*4)\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.ConvTranspose2d(\n",
    "                in_channels=1024, out_channels=512, kernel_size=4,\n",
    "                stride=2, padding=1, bias=False\n",
    "            ),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.ConvTranspose2d(\n",
    "                in_channels=512, out_channels=256, kernel_size=4,\n",
    "                stride=2, padding=1, bias=False\n",
    "            ),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.ConvTranspose2d(\n",
    "                in_channels=256, out_channels=128, kernel_size=4,\n",
    "                stride=2, padding=1, bias=False\n",
    "            ),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.ConvTranspose2d(\n",
    "                in_channels=128, out_channels=1, kernel_size=4,\n",
    "                stride=2, padding=1, bias=False\n",
    "            )\n",
    "        )\n",
    "        self.out = torch.nn.Tanh()\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Project and reshape\n",
    "        x = self.linear(x)\n",
    "        x = x.view(x.shape[0], 1024, 4, 4)\n",
    "        # Convolutional layers\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        # Apply Tanh\n",
    "        return self.out(x)\n",
    "    \n",
    "# Noise\n",
    "def noise(size):\n",
    "    n = Variable(torch.randn(size, 100))\n",
    "    if torch.cuda.is_available(): return n.cuda()\n",
    "    return n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_weights(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('Conv') != -1 or classname.find('BatchNorm') != -1:\n",
    "        m.weight.data.normal_(0.00, 0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Network instances and init weights\n",
    "generator = GenerativeNet()\n",
    "generator.apply(init_weights)\n",
    "\n",
    "discriminator = DiscriminativeNet()\n",
    "discriminator.apply(init_weights)\n",
    "\n",
    "# Enable cuda if available\n",
    "if torch.cuda.is_available():\n",
    "    generator.cuda()\n",
    "    discriminator.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimizers\n",
    "d_optimizer = Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
    "g_optimizer = Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
    "\n",
    "# Loss function\n",
    "loss = nn.BCELoss()\n",
    "\n",
    "# Number of epochs\n",
    "num_epochs = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_data_target(size):\n",
    "    '''\n",
    "    Tensor containing ones, with shape = size\n",
    "    '''\n",
    "    data = Variable(torch.ones(size, 1))\n",
    "    if torch.cuda.is_available(): return data.cuda()\n",
    "    return data\n",
    "\n",
    "def fake_data_target(size):\n",
    "    '''\n",
    "    Tensor containing zeros, with shape = size\n",
    "    '''\n",
    "    data = Variable(torch.zeros(size, 1))\n",
    "    if torch.cuda.is_available(): return data.cuda()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_discriminator(optimizer, real_data, fake_data):\n",
    "    # Reset gradients\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    # 1. Train on Real Data\n",
    "    prediction_real = discriminator(real_data)\n",
    "    # Calculate error and backpropagate\n",
    "    error_real = loss(prediction_real, real_data_target(real_data.size(0)))\n",
    "    error_real.backward()\n",
    "\n",
    "    # 2. Train on Fake Data\n",
    "    prediction_fake = discriminator(fake_data)\n",
    "    # Calculate error and backpropagate\n",
    "    error_fake = loss(prediction_fake, fake_data_target(real_data.size(0)))\n",
    "    error_fake.backward()\n",
    "    \n",
    "    # Update weights with gradients\n",
    "    optimizer.step()\n",
    "    \n",
    "    return error_real + error_fake, prediction_real, prediction_fake\n",
    "    return (0, 0, 0)\n",
    "\n",
    "def train_generator(optimizer, fake_data):\n",
    "    # Reset gradients\n",
    "    optimizer.zero_grad()\n",
    "    # Sample noise and generate fake data\n",
    "    prediction = discriminator(fake_data)\n",
    "    # Calculate error and backpropagate\n",
    "    error = loss(prediction, real_data_target(prediction.size(0)))\n",
    "    error.backward()\n",
    "    # Update weights with gradients\n",
    "    optimizer.step()\n",
    "    # Return error\n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_test_samples = 16\n",
    "test_noise = noise(num_test_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [22/200], Batch Num: [400/600]\n",
      "Discriminator Loss: 0.1392, Generator Loss: 4.4327\n",
      "D(x): 0.8839, D(G(z)): 0.0072\n"
     ]
    }
   ],
   "source": [
    "logger = Logger(model_name='DCGAN', data_name='MNIST')\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for n_batch, (real_batch,_) in enumerate(data_loader):\n",
    "        \n",
    "        # 1. Train Discriminator\n",
    "        real_data = Variable(real_batch)\n",
    "        if torch.cuda.is_available(): real_data = real_data.cuda()\n",
    "        # Generate fake data\n",
    "        fake_data = generator(noise(real_data.size(0))).detach()\n",
    "        # Train D\n",
    "        d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, \n",
    "                                                                real_data, \n",
    "                                                                fake_data)\n",
    "\n",
    "        # 2. Train Generator\n",
    "        # Generate fake data\n",
    "        fake_data = generator(noise(real_batch.size(0)))\n",
    "        # Train G\n",
    "        g_error = train_generator(g_optimizer, \n",
    "                                  fake_data)\n",
    "        # Log error\n",
    "        logger.log(d_error, g_error, epoch, n_batch, num_batches)\n",
    "        \n",
    "        # Display Progress\n",
    "        if (n_batch) % 100 == 0:\n",
    "            display.clear_output(True)\n",
    "            # Display Images\n",
    "            test_images = generator(test_noise).data.cpu()\n",
    "            logger.log_images(test_images, num_test_samples, epoch, n_batch, num_batches);\n",
    "            # Display status Logs\n",
    "            logger.display_status(\n",
    "                epoch, num_epochs, n_batch, num_batches,\n",
    "                d_error, g_error, d_pred_real, d_pred_fake\n",
    "            )\n",
    "        # Model Checkpoints\n",
    "        logger.save_models(generator, discriminator, epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pytorch2] *",
   "language": "python",
   "name": "conda-env-pytorch2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
